Nothing
# LUCID - five omics, normal outcome
test_that("check predictions of LUCID with normal outcome (K = 2,2,2,2,2)", {
# run LUCID model
i <- 1008
set.seed(i)
G <- matrix(rnorm(500), nrow = 100)
Z1 <- matrix(rnorm(1000),nrow = 100)
Z2 <- matrix(rnorm(1000), nrow = 100)
Z3 <- matrix(rnorm(1000), nrow = 100)
Z4 <- matrix(rnorm(1000), nrow = 100)
Z5 <- matrix(rnorm(1000), nrow = 100)
Z <- list(Z1 = Z1, Z2 = Z2, Z3 = Z3, Z4 = Z4, Z5 = Z5)
Y <- rnorm(100)
CoY <- matrix(rnorm(200), nrow = 100)
CoG <- matrix(rnorm(200), nrow = 100)
# i <- sample(1:2000, 1)
# cat(paste("test1 - seed =", i, "\n"))
invisible(capture.output(fit1 <- est_lucid(G = G, Z = Z, Y = Y, K = c(2, 2, 2, 2, 2),
CoG = CoG, CoY = CoY,
lucid_model = "parallel",
init_omic.data.model = "VVV",
family = "normal",
seed = i,
useY = TRUE)))
#use training data
pred1 <- predict_lucid(model = fit1,
lucid_model = "parallel",
G = G,
Z = Z,
Y = Y,
CoG = CoG, CoY = CoY)
expect_equal(fit1$inclusion.p, pred1$inclusion.p, tolerance = 0.05)
expect_equal(class(pred1$pred.x), "list")
set.seed(i+1000)
n_G <- matrix(rnorm(500), nrow = 100)
n_Z1 <- matrix(rnorm(1000),nrow = 100)
n_Z2 <- matrix(rnorm(1000), nrow = 100)
n_Z3 <- matrix(rnorm(1000), nrow = 100)
n_Z4 <- matrix(rnorm(1000), nrow = 100)
n_Z5 <- matrix(rnorm(1000), nrow = 100)
n_Z <- list(Z1 = n_Z1, Z2 = n_Z2, Z3 = n_Z3, Z4 = n_Z4, Z5 = n_Z5)
n_Y <- rnorm(100)
n_CoY <- matrix(rnorm(200), nrow = 100)
n_CoG <- matrix(rnorm(200), nrow = 100)
#use new data
pred2 <- predict_lucid(model = fit1,
lucid_model = "parallel",
G = n_G,
Z = n_Z,
Y = n_Y,
CoG = n_CoG, CoY = n_CoY)
expect_equal(class(pred2$pred.x), "list")
expect_equal(mean(pred2$pred.y), 0.03375455, tolerance = 0.05)
expect_equal(mean(pred2$inclusion.p[[1]]), 0.5)
pred3 <- predict_lucid(model = fit1,
lucid_model = "parallel",
G = n_G,
Z = n_Z,
Y = NULL,
CoG = n_CoG, CoY = n_CoY)
expect_equal(class(pred3$pred.x), "list")
expect_equal(mean(pred3$pred.y), 0.03375455, tolerance = 0.05)
expect_equal(mean(pred3$inclusion.p[[1]]), 0.5)
})
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